When I used to work in Rwanda, I lived on a small street in Kigali. Every time I invited friends over, I would tell them to “walk past the Embassy, look out for the Church, and then continue to the house with the black gate.” The day a street sign was erected on my street was a game changer.

So how do more than two million citizens of Accra navigate the busy city without the help of street names? While some street names are commonly known, most streets do not have any official name, street sign or house number. Instead, people usually refer to palm trees, speed bumps, street vendors, etc.

But, what happens when the palm tree is cut or when the street vendor changes the location?

The absence of street names poses not only challenges for orientation, but also for property tax collection, postal services, emergency services, and the private sector. Especially, new economy companies, such as Amazon or Uber, depend on street addressing systems and are eager to cater to market demands of a growing middle class.

To address these challenges, the Accra Metropolitan Assembly (AMA), financed by the World Bank’s second Land Administration Project , is implementing a street addressing and property numbering system in Accra. Other Metropolitan areas received funding from other World Bank-funded projects for similar purposes.

In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.

Can we rely only on satellite? How accurate are these results?

It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.

So, it’s important to learn how well the resulting maps reflect the reality on the ground.

Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.

Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.

Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).

The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.

But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.

In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.

A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.

Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.

What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.

My previous blog post surveyed some of the recent trends in developing global measures of urbanization. In this post, I want to turn to a brief discussion for scholars and practitioners on some possible applications and areas of focus for ongoing work:

While there are a number of different maps for documenting urban expansion, each has different strengths and weaknesses in application. Coarser resolution maps such as MODIS can be used for mapping the basic contours of artificial built-up areas in regional and comparative scales. On the other hand, high-resolution maps are best suited for individual cities, as algorithms can be used to identify and classify observed colors, textures, shading, and patterns into different types of land uses. These levels of detail are difficult to use for reliable comparisons between cities as the types of building materials, structure shapes, light reflectivity, and other factors can vary widely between countries and regions.

Nonetheless, there are a number of applications for policymakers in this regard, from identifying and mapping green spaces and natural hazard risks to identifying and tracking areas of new growth, such as informal settlements. However, such approaches to land use detection require careful calibration of these automated methods, such as cross referencing with other available maps, or by “ground truthing” with a sample of street-level photos of various types of buildings and land cover as reference inputs for automation. One solution to this is the use of social media and geo-coded data to confirm and monitor changes in urban environments alongside the use of high-resolution satellite imagery.

Nighttime light maps also have gained traction as measures of urban extent and as ways to gauge changes in economic activity in large urban centers. They are probably less useful for documenting smaller settlements, which may be dimmer or have little significant variation in brightness. It is important to correct these types of maps for “overglow” measurement effects—where certain light may “bleed” or obscure the shapes and forms of very large, bright urban areas in relation to adjacent smaller and dimmer settlements (newer VIIRs maps have made some important advances in correcting this).

This satellite image shows Sao Paolo's estimated “urban areas” based on a WorldPop gridded population layer. Areas in yellow are areas with at least 300 people per km2 and a known settlement size of 5,000 people. Red areas represent a population density threshold of at least 1,500 people per km2 and a known settlement size of 50,000 people.
There remains a surprising amount of disagreement over precisely what “urban” means despite the ubiquity of the term in our work. Are urban areas defined by a certain amount of artificial land cover such as permanent buildings and roads? Or are they more accurately described as spatially concentrated populations? The answer often depends on what country you are in, as their administrative definitions of urban areas can vary widely across and between these two dimensions.

The UN’s World Urbanization Prospects (WUP), perhaps the most comprehensive and widely cited measure of urbanization across the world, draws from a compilation of country-level population totals based on administrative definitions. A key weakness with this set is that since each country defines “urban” differently, it is difficult to accurately compare one country’s urbanization to another, as well as to estimate the urban population of a group of countries or the world itself. Recent work has provided more sophisticated ways to measure urban growth and expansion using both satellite map data and careful application of population data.

In this video, representatives from the World Bank, GEF, and City of Johannesburg discuss the impact of geospatial tools on urban planning.
Many urban residents these days will find it hard to imagine a life without mobile apps that help us locate a restaurant, hail a cab, or find a subway station—usually in a matter of seconds. If geospatial technology and data already make our everyday lives this easier, imagine what they can do for our cities: for example, geospatial data on land-use change and built-up land expansion can provide for more responsive urban planning, while information on traffic conditions, road networks, and solid waste sites can help optimize management and enhance the quality of urban living.

The “urban geo-data gap”

However, information and data that provide the latest big picture on urban land and services often fail to keep up with rapid population growth and land expansion. This is especially the case for cities in developing countries—home to the fastest growing urban and vulnerable populations.

Growing up on a farm meant I spent very little time in cities. I felt more at home when surrounded by green than grey. As a kid, I saw cities as noisy, bright, busy and quite frankly, confusing. I always thought to myself why would anyone want to live in them? However, when I grew up, I moved to a city to take advantage of the opportunities it provided. I am not alone. More than 50 percent of the world’s population lives in cities and this number will rise. Cities are hubs of productivity, innovation and vast human capital; but once you live in them you begin to see that they are like any other ecosystem: complex and fragile, whose balance can be easily disturbed. With many cities rapidly growing and evolving, how do you manage this increasing complexity without destroying the ecosystem?

Geographical Information System (GIS) techniques have proven successful in mapping, analyzing and managing natural ecosystems. It is now time to make use of the same technology to manage, model and design our expanding global system of cities. GIS consists of a proven set of tools that can provide information to leaders at the local and national level to facilitate evidence-driven decision making. It allows us to move beyond 2D paper maps and incorporate everything that lies below, above and around a city to create a 3D digital representation of the city’s ecosystem. By integrating this information into the planning process, it will hopefully lead to harmonized planning across sectors. For example, integrated transport and land use planning and development will allow for economic, social and environmental benefits. More sectors can then be incorporated, with this integration not only happening within the city limits but including the urban periphery, where a lot of urban expansion is currently occurring. This holistic view will allow planners to make cities more livable.

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The Sustainable Cities blog is a space for urban development professionals to exchange ideas and engage some of the central questions of sustainable cities: What makes a sustainable city? How do we measure a city's sustainability? More ...

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